Human-AI collaborative multi-modal multi-rater learning for endometriosis diagnosis

Objective.Endometriosis, affecting about 10% of individuals assigned female at birth, is challenging to diagnose and manage. Diagnosis typically involves the identification of various signs of the disease using either laparoscopic surgery or the analysis of T1/T2 MRI images, with the latter being quicker and cheaper but less accurate. A key diagnostic sign of endometriosis is the obliteration of the pouch of Douglas (POD). However, even experienced clinicians struggle with accurately classifying POD obliteration from MRI images, which complicates the training of reliable AI models.Approach.In this paper, we introduce theHuman-AICollaborativeMulti-modalMulti-rater Learning (HAICOMM) methodology to address the challenge above. HAICOMM is the first method that explores three important aspects of this problem: (1) multi-rater learning to extract a cleaner label from the multiple 'noisy' labels available per training sample; (2) multi-modal learning to leverage the presence of T1/T2 MRI images for training and testing; and (3) human-AI collaboration to build a system that leverages the predictions from clinicians and the AI model to provide more accurate classification than standalone clinicians and AI models.Main results.Presenting results on the multi-rater T1/T2 MRI endometriosis dataset collected for validating the methodology, the proposed HAICOMM model outperforms an ensemble of clinicians, noisy-label learning models, and multi-rater learning methods by a large margin.Significance.The HAICOMM methodology offers a novel solution to the long-standing problem of accurately diagnosing endometriosis from MRI images, specifically in relation to the key diagnostic sign of POD obliteration. By leveraging multi-rater, multi-modal, and human-AI collaborative learning, it has the potential to improve the accuracy of endometriosis diagnosis, which could have far-reaching implications for the better management of this challenging medical condition that affects a significant proportion of the female population.